Paper Detail
Jiehui Huang, Yuechen Zhang, Bin Xia, Jiahao Wang, Xu He, Zhenchao Tang, Meng Chu, Xin Tao, Pengfei Wan, Jiaya Jia
Generating a coherent multi-shot video requires structured cross-shot memory. Subject appearance, scene context, and speaker identity must persist across cuts. Existing approaches either train end-to-end over fixed-length sequences and cannot scale, generate shot-by-shot with memory banks that grow linearly, or orchestrate pretrained generators under an LLM planner without a multi-shot-aware backbone. We present UnityShots, a memory-driven multi-shot audio-video generation system built on LTX-2.3, trained on annotated cinematic and music-video shots. The video stream maintains two fixed-size slots, a long-term memory (LTM) slot anchored to the opening shot and a short-term memory (STM) slot holding the immediately preceding tail, both updated at every cut by a boundary-conditioned gate that fuses visual cut probability and beat-tracker signals. The audio stream injects a reference speaker token at every shot to preserve vocal timbre without a sliding audio bank. A discrete cut-type prior, learned through AdaLN, becomes an inference-time control knob over transition strength. We release a benchmark of 200 multi-cultural multi-shot sequences spanning six ethnic regions and ten or more languages, with per-shot reference identities, reference audio, and per-boundary transition labels. Evaluated across I2V, T2V, and R2V conditioning modes, UnityShots leads open-source baselines on every cross-shot coherence metric and matches the strongest closed-source system on the multi-shot axes.
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@misc{huang2026unityshots,
title = {UnityShots: Memory-Driven Multi-Shot Audio-Video Generation with Boundary-Aware Gating},
author = {Jiehui Huang and Yuechen Zhang and Bin Xia and Jiahao Wang and Xu He and Zhenchao Tang and Meng Chu and Xin Tao and Pengfei Wan and Jiaya Jia},
year = {2026},
abstract = {Generating a coherent multi-shot video requires structured cross-shot memory. Subject appearance, scene context, and speaker identity must persist across cuts. Existing approaches either train end-to-end over fixed-length sequences and cannot scale, generate shot-by-shot with memory banks that grow linearly, or orchestrate pretrained generators under an LLM planner without a multi-shot-aware backbone. We present UnityShots, a memory-driven multi-shot audio-video generation system built on LTX-2.},
url = {https://huggingface.co/papers/2606.21661},
keywords = {multi-shot audio-video generation, LTX-2.3, long-term memory, short-term memory, boundary-conditioned gate, visual cut probability, beat-tracker signals, reference speaker token, discrete cut-type prior, AdaLN, cross-shot coherence, code available, huggingface daily},
eprint = {2606.21661},
archiveprefix = {arXiv},
}
{}